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Every year, American high school students take SATs, which are standardized tests intended to measure literacy, numeracy, and writing skills. There are three sections - reading, math, and writing, each with a maximum score of 800 points. These tests are extremely important for students and colleges, as they play a pivotal role in the admissions process.
Analyzing the performance of schools is important for a variety of stakeholders, including policy and education professionals, researchers, government, and even parents considering which school their children should attend.
You have been provided with a dataset called schools.csv, which is previewed below.
You have been tasked with answering three key questions about New York City (NYC) public school SAT performance.
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
print(schools.info())
# Start coding here...
higher_than_640 = schools[schools['average_math']>640]
best_math_schools = higher_than_640[['school_name', 'average_math']].sort_values('average_math', ascending=False)
#print(best_math_schools)
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending=False)
top_10_schools = top_10_schools.head(10)
#print(top_10_schools)
new_index = schools.set_index('borough')
total_SAT = new_index.groupby('borough')['total_SAT'].sum()
num_schools = new_index.groupby('borough')['school_name'].count()
stats = round(new_index.groupby('borough')['total_SAT'].agg(['mean', 'std']), 2)
merge = num_schools.to_frame().join(stats)
merge2 = total_SAT.to_frame().join(merge)
print(merge2)
largest_std_dev = merge2.rename(columns={'school_name':'num_schools', 'mean':'average_SAT', 'std':'std_SAT'})
largest_std_dev = largest_std_dev[largest_std_dev['std_SAT']==largest_std_dev['std_SAT'].max()]
print(largest_std_dev)
# Add as many cells as you like...